1. Hosted Jupyter Notebooks for Data Analysis on AWS Lightsail


    To create infrastructure that hosts Jupyter Notebooks for data analysis on AWS Lightsail, you need to provision an instance that can run a Jupyter server and make it accessible over the internet. AWS Lightsail is a service that simplifies the process of launching and managing a virtual private server and is suitable for such a small-scale and cost-effective application.

    Here's how you can get started with Pulumi to create such an environment using Python:

    1. Create an AWS Lightsail Instance: You'll need an instance that's capable of running a Jupyter Notebook server. For data analysis, you might want an instance with a bit more CPU and memory. You'll need to choose a blueprint (similar to an AMI in EC2) that has the required software, like Python and Jupyter.

    2. Access and Security: To access your Jupyter server, you'll need to configure the networking for your instance to allow traffic on the port that Jupyter will run on (commonly port 8888). You may also wish to restrict access to certain IP addresses for security purposes.

    3. Configure the Instance: Once you have your instance, you'll need to set it up with the necessary software and configurations. You can use the instance's userData property to pass in a shell script that runs on first boot. This script can install Jupyter and any other desired packages, start up your Jupyter server, and set it up to start on reboot.

    4. DNS Configuration: Optionally, you can configure a domain name to point to this instance to make it easy to access.

    5. Set Up Persistence: Data analysis often involves working with datasets that you don't want to lose if your instance is stopped or restarted. AWS Lightsail has block storage that you can attach to an instance for persistent storage.

    6. Backups: It's a good practice to set up automatic snapshots of your instance to back up your work.

    7. Pulumi Stack Outputs: After deployment, you will want to output the public IP address or domain name of the instance so you know where to connect to your Jupyter Notebook server.

    Here is a program that does this:

    import pulumi import pulumi_aws as aws # Create a new Lightsail instance that is capable of running Jupyter Notebooks jupyter_instance = aws.lightsail.Instance("jupyter-notebook-instance", availability_zone="us-west-2a", # Choose the right AWS availability zone blueprint_id="python_3", # This blueprint id represents a vanilla Python instance bundle_id="medium_2_0", # Instance plan that fits the need for data analysis (customize this as needed) key_pair_name="jupyter-keypair", # Reference to a key pair to securely SSH into the instance # User data script that installs Jupyter and starts it upon boot user_data="""#!/bin/bash pip install jupyter # You might want to include more setup steps here jupyter notebook --ip= --port=8888 --no-browser --NotebookApp.token='' --NotebookApp.password='' """ ) # Export the public IP address of the Jupyter Notebook instance pulumi.export('jupyter_ip', jupyter_instance.public_ip_address)

    In this program:

    • aws.lightsail.Instance creates a new instance where we specify the availability zone, blueprint ID for the OS, instance bundle type which determines the CPU and memory, the key pair name for SSH access, and a user data script that installs Jupyter Notebook server and runs it. Note that in the user_data section, we're running Jupyter to listen on all IPs on port 8888. We've also disabled authentication for simplicity; in a production environment, you'd want to set up a token or password.

    • pulumi.export outputs the public IP address that you can use to access your Jupyter Notebook server once the instance is running.

    Before running the program, you need to have the Pulumi CLI installed, an account created, and the AWS CLI configured with your credentials. Save this Python code in a file named __main__.py, ensure you have all required Python packages installed (using a virtual environment is recommended), and run pulumi up. This will show you the changes before they are applied, ask for confirmation, and proceed to provision the infrastructure.

    To enhance this setup, consider:

    • Attaching a block storage to your instance for persistent data storage.
    • Setting up automatic snapshots for backups using aws.lightsail.InstanceSnapshot.
    • Integrating with your domain using aws.lightsail.Domain to give a friendly URL to access the Jupyter Notebook server.

    Remember to SSH to the instance to start the Jupyter Notebook server if it doesn't start automatically, and ensure your security settings allow you to access it over the internet.